• Journal of the Korean Society for Library and Information Science
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• v.30 no.4
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• pp.39-60
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• 1996

A variety of searcher traits, characteristics, subject background and behaviors have been the subject of investigations exploring various hypotheses relating to searching performances. Previous studies have focused on searchers personal characteristics such as training, experience, subject knowledge, intelligence, cognitive style, attitude and searching style, Each of these factors is examined in this paper in order to find out searcher's personal characteristics affecting searching performance. Surprisingly, searching training and experience have not been found to influence searching performance. The hypothesis that intellectual ability correlates with the ability to online search seems to have little effect Various cognitive styles of searchers were tested to find out whether they relate to search results. Only FD/Fl cognitive style were found to be significant in relation to search results. Searchers showed a variety of attitudes about online searching. They revealed sensitivity toward searching charges. The attitude toward charges was reflected on the searching behavior. The sensitive searchers tend to conduct cost-effective searches, Searching styles of intermediaries were characterized as interactive and fast batch. It was found that experienced searchers prefer simple searches which do not explore the interactive capabilities of online system. In summary, previous studies have confirmed that there are apparently great individual differences among online searchers in searching behaviors as well as attitudes. But relationships between these individual differences and search performance were too weak to be significant.

• Journal of the Korean Geographical Society
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• v.49 no.4
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• pp.615-632
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• 2014

This study aims to explore 'bluffing', one of the most important topics in order to ensure the objectivity, validity, reliability of scoring of constructed-response items. The author identifies the conception of bluffing, and classifies major types of bluffing on the basis of previous studies on the theoretical level. Next, the author analyzes empirically the bluffing strategies and types of learners on key terms of Korean Geography subject. Compared with the existing research reports, the result of this study shows a significantly lower average bluffing score. On the other hand, it is consistent in results of previous studies reported that average bluffing score is similar between genders and that those students who got highest grades did least bluffing. Actually bluffing types are classified into four categories: 'repeating the question' type, 'depending on a well-known or existing knowledge' type, 'applying some basic concepts regardless of understanding' type, and 'inducing scorer's basic beliefs' type. Some comments and suggestions are as follows. First, it is necessary to continue researches of the relations among bluffing and age, gender, grade, intelligence and learning styles. Second, it is required for scorers who score constructed-response items to develop and supply the scoring guide including analysis contents of bluffing types and cases, and increase opportunity for training. Third, we need to inquire the domain-specific bluffing types in geography subject based on the generalizable sample size.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• Informatization Policy
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• v.26 no.4
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• pp.62-84
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• 2019

The purpose of this study is to present improvement measures for youth activity policies that can change the youth activity programs, equipment and facility operation and leaders in order to cultivate youth capacities suitable for the intelligent information society. For this purpose, we conducted a literature study on the direction of youth activity policy changes with the forecasted social changes in the intelligence information society and an online survey and expert opinion surveys to identify how youth activity facilities are currently coping with the changes and to explore measures to improve youth activity policies. The research identified 17 policy tasks - 9 tasks for the program area, including 1) increase of intelligent information technology related programs, 2) increase of contents related to career resilience in career experience and training programs, and 3) systematic introduction and diffusion of STEAM and Maker education programs; 5 tasks for the equipment and facility operation area, including 1) strengthening cooperation network with external organizations related to the intelligent information technologies, 2) expansion of AR and VR technology application in activity program development, improvement and operation, and 3) big data building in the field of youth activities; and 3 tasks for the leaders area, including 1) extension of information provision on the intelligent information society to the leaders of the activity facilities, and 2) development of job models related to the intelligent information society and job training.

• Spatial Information Research
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• v.22 no.3
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• pp.79-87
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• 2014

In order to foster rapid disaster response and public life protection, National Emergency Management Agency has been trying to spread 'Emergency Rescue Standard System' on a national scale since 2006. The agency has also intensified management of firefighter's safety on disaster site by implementing danger predication training, specialized training and education and safety procedure check as a part of safety management officer duties. Nevertheless, there are limitations for effective fire fighting steps, such as damage spreading and life damage due to unawareness of illegal converted structure, structure transformation by high temperature and nearby hazardous material storage as well as extemporary situation handling endangered firefighter's life. In order to eliminate these limitations there is a need for an effort and technology application to minimize human errors such as inaccurate situational awareness, wrong decision built on experience and judgment of field commander and firefighters. The purpose of this study is to propose a new disaster response model which is applied with geospatial information. we executed spatial contextual awareness map analysis using fire-fighting vulnerable zone model to propose the new disaster response model and also examined a case study for Dalseo-gu in Daegu Metropolitan City. Finally, we also suggested operational concept of new proposed model on a national scale.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.93-116
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• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• Journal of Physiology & Pathology in Korean Medicine
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• v.33 no.5
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• pp.255-260
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• 2019

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(So-Yang, So-Eum and Tae-Eum) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 32 students participating in Sasang medical practice(12 subjects were So-Yang, 7 subjects were So-Eum, and 13 subjects were Tae-Eum). We analyze subject's clinical practice result reports by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, Bing-xie Pattern Identification, and Visceral Pattern Identification in each Sasang types (So-Yang, So-Eum, Tae-Eum). In the case of Heat-moisture, Tae-Eum showed higher score than So-Eum, but So-Yang showed no difference from the other two Sasang types(p<0.05). And in the case of Food-accumulation, Tae-Eum and So-Yang showed significantly higher score than So-Eum(p<0.05). It is hard to generalize the result because subject of this study was not enough. However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. Therefore use of ODS program in student clinical practice training help to understand the relationship and correlation between different pattern Identification and will help standardization of clinical practice education.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Therapeutic Science for Rehabilitation
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• v.11 no.2
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• pp.63-76
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• 2022

Objective : The purpose of this study is to examine the relationship with employment of the disabled considering the severity and the type of disability. Methods : Data from the 4th data of the 2nd wave Panel Survey of Employment for the Disabled (PSED) by Korea Employment Agency for Persons with Disabilities (KEAD) were used. The odds ratio of employment in disability types according to severity of disability was calculated by logistic regression analysis. Results : When the related variables were adjusted, the employment of internal disability type was significantly lower than that of external disability type by 0.413(95% CI:0.271-0.629) times in the group with severe disability. On the other hand, in the group with less severe disability, internal disability was 0.475(95% CI:0.327-0.690) times lower than that of external disability (p=<.001). Conclusions : Employment may vary depending on the type of disability, even if the disability severity level is the same. It is necessary to prepare judgment criteria that can reduce the variation in employment by considering both the type and severity of the disability.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.251-260
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• 2022

Although the number of trees affected by pine wilt disease is decreasing, the affected area is expanding across the country. Recently, with the development of deep learning technology, it is being rapidly applied to the detection study of pine wilt nematodes and dead trees. The purpose of this study is to efficiently acquire deep learning training data and acquire accurate true values to further improve the detection ability of U-Net models through learning. To achieve this purpose, by using a filtering method applying a step-by-step deep learning algorithm the ambiguous analysis basis of the deep learning model is minimized, enabling efficient analysis and judgment. As a result of the analysis the U-Net model using the true values analyzed by period in the detection and performance improvement of dead pine trees of wilt nematode using the U-Net algorithm had a recall rate of -0.5%p than the U-Net model using the previously provided true values, precision was 7.6%p and F-1 score was 4.1%p. In the future, it is judged that there is a possibility to increase the precision of wilt detection by applying various filtering techniques, and it is judged that the drone surveillance method using drone orthographic images and artificial intelligence can be used in the pine wilt nematode disaster prevention project.